Due to the explosive growth in data traffic, machine learning and data driven approaches have recently received much attention and are anticipated to be a key enabler for the to be developed sixth generation (6G) wireless networks [1]}.
Nowadays, standard machine learning approaches require centralizing the training data on a single data center or cloud.
Since massive data samples need to be uploaded to the data center, transmission delay can be very high and user privacy is not guaranteed in standard centralized machine learning approaches.
However, low-latency and privacy requirements are important in the emerging application scenarios, such as unmanned aerial vehicles, extended reality (XR) services, autonomous driving, which makes centralized machine learning approaches inapplicable.
Moreover, due to limited communication resources, it is impractical for all the wireless devices
that are engaged in learning to transmit all of their collected
data to a data center that uses
a centralized learning algorithm for data analytic or network
self-organization.
